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About the Role We are looking for an AI Engineer with 2+ years of experience , with strong expertise in Computer Vision and Deep Learning . The role involves developing, experimenting with, and deploying AI solutions, with a primary focus on image and video-based applications and applied research. Experience: 2+ Years Employment Type: Full-time Location: Kanpur Key Responsibilities Design, develop, train, evaluate, and deploy AI/ML and Computer Vision models . Develop end-to-end vision pipelines covering data preparation, model development, training, evaluation, optimization, and deployment . Work on problems such as object detection, image classification, segmentation, object tracking, OCR, image/video analytics, and anomaly detection . Research and evaluate new Computer Vision models, architectures, algorithms, and techniques . Read and implement ideas from research papers and technical publications . Develop PoCs and prototypes to validate new approaches. Optimize models for accuracy, latency, scalability, and resource efficiency . Work with image/video datasets , including data preprocessing, augmentation, annotation, and dataset management. Deploy AI solutions across GPU, CPU, cloud, and edge environments where required. Collaborate with engineering and domain teams to integrate AI solutions into products and platforms. Contribute to technical reports, research publications, patents, and R&D initiatives , where applicable. Required Skills Strong proficiency in Python and good software engineering fundamentals. Strong understanding of Computer Vision, Machine Learning, and Deep Learning . Hands-on experience with PyTorch, TensorFlow, OpenCV, or equivalent frameworks . Practical experience with CNNs, Vision Transformers, YOLO, or similar vision architectures . Strong hands-on experience in one or more areas including object detection, classification, segmentation, tracking, OCR, or video analytics . Experience with image/video processing and computer vision pipelines . Understanding of model training, evaluation, optimization, and performance benchmarking. Familiarity with GPU computing, CUDA, ONNX, TensorRT, or edge AI is desirable. Familiarity with Git, Linux, Docker, REST APIs, and model deployment is preferred. Ability to read, understand, and implement research papers . Strong analytical, problem-solving, and research skills. Qualifications & Experience Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Electronics, or a related field . 2+ years of relevant professional experience in AI/ML, with significant hands-on experience in Computer Vision . Demonstrated experience developing at least one end-to-end Computer Vision solution . Experience working with real-world image/video datasets and production AI systems is preferred. Research publications, patents, open-source contributions, or significant Computer Vision projects are a plus.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.